Overview

This is a combination of these analyses used for loop. Code is a combination of Daniela and Shuyings code. Might add Nicks later

Below is a list of sections included here. Including summaries of the white matter analyses and completed figures.




Methods

Behavior

  • group differences?

Hormones

Shapiro-Wilk normality tests were conducted to assess violations of normality of the independent and dependent variables before conducting correlations between spatial navigation dependent variables and sex hormones. To control for chronological age while assessing the relationship between sex steroid hormones and navigational strategy, partial Spearman rank correlations were conducted if the Shapiro-Wilk normality tests were statistically significant (p < 0.05); otherwise, Pearson correlations were conducted. Based on existing evidence of sex hormones’ influence on navigation from the animal literature and strong a priori predictions that estradiol would be positively associated with navigation performance, and follicle-stimulating hormone would show an opposing effect, we conducted one-tailed analyses for these tests, controlling for chronological age. Two-tailed analyses were used for hormones without a strong a priori hypothesis (progesterone, testosterone). Men and women were analyzed separately. For men, we conducted a one-tailed correlation for testosterone to be in the positive direction, while we conducted a two-tailed correlation for testosterone for women.

Hippocampal Subfields

** notes as of 2025 ** - T1 HP volume extracted using freesurfer recon all. - Corrected using TIV from free surfer

Total hippocampus (from T1-weighted whole brain scans) and hippocampal subfield volumes were corrected using participant’s total intracranial volume (TIV) to remove size Figure 4.1. Investigating volumetric differences using segmentation of the medial temporal lobe and total hippocampus region. (A) Sample slice of the medial temporal lobe cortex and hippocampus segmented into hippocampal subfields using the Automatic Segmentation of Hippocampal Subfields software. Labeled subfields include: CA1 (cornu ammonis), CA2/3, DG (dentate gyrus), SUB (subiculum), ERC (entorhinal cortex), PRC (perirhinal cortex), and PHC (parahippocampal cortex). Total hippocampus is computed by aggregating subfields CA1, CA2/3, DG, and SUB. Medial temporal lobe is computed by aggregating all the subfields. (B) Women (n = 74, M = 0.56) tend to have larger T1 total hippocampal volume than men (n = 32, M = 0.48; t(68) = 9.72, p < 0.001). Boxplot endpoints indicate the 25th and 75th percentile, and the black line within the boxplot indicates the median value while the black point within the boxplot indicates the mean value. p-values: *** p < 0.001.

bias in comparisons. In addition to the total hippocampal volume from the T1-weighted scans, another measure of total hippocampal volume from the T2-weighted hippocampal subfield scans was calculated by taking the sum of the CA1, CA2/3, dentate gyrus, and subiculum subfield volumes after adjusted for TIV. These structures make up the hippocampus region based on the anatomical components of the medial temporal lobe system (Squire et al., 2004; Squire & Zola-Morgan, 1991). An average of the left and right grey matter volume (mm3) for the total hippocampus and the individual subfields was used for analysis. For all the statistical tests mentioned, corrections for multiple comparisons were performed using Benjamini, Hochberg, and Yekutieli p-adjustments to control the false discovery rate.


Cortical Thickness

  • Conduct Spearman correlations
  • Benjamini Hotchkins control
  • Going to try Q value control

Diffusion



Results

Reading in and prepping our data

Reading in our main LOOP CSV and creating large dataframes for midlife and young




Hippocampal Volume

  • Need to figure out how many comparisons are included to use the BY correction

Midlife

List of columns

The list below are from shuyings original raw data. We will ignore the old T1. The T2 here are already corrected for TIV.

columns
t1_vbm_tiv
t1_vbm_gmv
t1_vbm_wmv
t1_vbm_csf
t1_vol_left_hipp_aal_2d_d1_r
t1_vol_right_hipp_aal_2d_d1_r
t1_vol_left_hipp_aal_3d_d1_s
t1_vol_right_hipp_aal_3d_d1_s
t2hipp_vol_avg_ca1
t2hipp_vol_avg_ca23
t2hipp_vol_avg_dg
t2hipp_vol_avg_erc
t2hipp_vol_avg_phc
t2hipp_vol_avg_prc
t2hipp_vol_avg_sub
t2hipp_vol_left_ca1
t2hipp_vol_left_ca23
t2hipp_vol_left_dg
t2hipp_vol_left_erc
t2hipp_vol_left_phc
t2hipp_vol_left_prc
t2hipp_vol_left_sub
t2hipp_vol_right_ca1
t2hipp_vol_right_ca23
t2hipp_vol_right_dg
t2hipp_vol_right_prc
t2hipp_vol_right_sub

Creating HP specific Dataframe : midlife_HP_df

Total N - 43

# Let's create a clean df to work with here and include only the columns we want 


midlife_HP_df <-
  midlife_raw_df %>%  dplyr::select(
    "subject_id",
    "sex",
    "age_spatial_years",
    "repo_status",
    "loop_pe_rad3_m",
    "loop_pe_avg_m",
    "loop_de_rad3_degree",
    "loop_de_avg_degree",
    "loop_ae_rad3_degree",
    "loop_ae_avg_degree",
    "t1_vbm_tiv",
    "t1_vbm_gmv",
    "t1_vbm_wmv",
    "t1_vbm_csf",
    "t1_vol_left_hipp_aal_2d_d1_r",
    "t1_vol_right_hipp_aal_2d_d1_r",
    "t1_vol_left_hipp_aal_3d_d1_s",
    "t1_vol_right_hipp_aal_3d_d1_s",
    "t2hipp_vol_avg_ca1",
    "t2hipp_vol_avg_ca23",
    "t2hipp_vol_avg_dg",
    "t2hipp_vol_avg_erc",
    "t2hipp_vol_avg_phc",
    "t2hipp_vol_avg_prc",
    "t2hipp_vol_avg_sub",
    "t2hipp_vol_left_ca1" ,
    "t2hipp_vol_left_ca23",
    "t2hipp_vol_left_dg",
    "t2hipp_vol_left_erc",
    "t2hipp_vol_left_phc",
    "t2hipp_vol_left_prc",
    "t2hipp_vol_left_sub"  ,
    "t2hipp_vol_right_ca1",
    "t2hipp_vol_right_ca23",
    "t2hipp_vol_right_dg",
    "t2hipp_vol_right_prc",
    "t2hipp_vol_right_sub",
    "Left-Hippocampus",
    "Right-Hippocampus",
    "eTIV",
    "estradiol_scan_pg_ml",
    "progesterone_scan_ng_ml",
    "fsh_scan_miu_ml",
    "shbg_scan_nmol_l",
    "dheas_scan_ug_dl",
    "testosterone_scan_ng_dl",
    "estradiol_spatial_pg_ml",
    "progesterone_spatial_ng_ml",
    "fsh_spatial_miu_ml",
    "shbg_spatial_nmol_l",
    "dheas_spatial_ug_dl",
    "testosterone_spatial_ng_dl"
  ) %>% mutate(avg_t1_hipp = (.$`Left-Hippocampus` + .$`Right-Hippocampus`) /
                 2) %>% filter(!is.na(eTIV)) %>% mutate(
                   avg_t2_total_hipp = t2hipp_vol_avg_ca1 + t2hipp_vol_avg_ca23 + t2hipp_vol_avg_dg + t2hipp_vol_avg_sub,
                   left_t2_total_hipp = t2hipp_vol_left_ca1 + t2hipp_vol_left_ca23 + t2hipp_vol_left_dg + t2hipp_vol_left_sub,
                   right_t2_total_hipp = t2hipp_vol_right_ca1 + t2hipp_vol_right_ca23 + t2hipp_vol_right_dg + t2hipp_vol_right_sub
                 )  # N=43

midlife_HP_female_df <- midlife_HP_df %>% filter(sex=="Female")

midlife_HP_male_df <- midlife_HP_df %>%  filter(sex== "Male")
 

Checking normality

  • Degrees traveled rad3 for all midlife is not normally distributed (p=0.04) and degrees traveled for men only ( p = 0.0001979)
  • Position error was normally distributed for all groups
  • Angular error rad3 was normally distributed for alll groups
  • Angular error average for midlife was not normally distributed (p = 0.0007472) and it was not normally distributed for women only ( p-value = 0.004246)
knitr::kable(normality_midlife_HP) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
statistic pvalue method variable
0.979930524959475 0.701056792400381 Shapiro-Wilk normality test midlife_HP_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.969511101559754 </td> <td style="text-align:left;"> 0.632694357678411 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_female_df\)loop_pe_rad3_m
0.980825888472044 0.979309814230114 Shapiro-Wilk normality test midlife_HP_male_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.958951620422575 </td> <td style="text-align:left;"> 0.126913086034452 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_df\)loop_pe_avg_m
0.932701897868634 0.0898823823710984 Shapiro-Wilk normality test midlife_HP_female_df\(loop_pe_avg_m </td> </tr> <tr> <td style="text-align:left;"> 0.959742019099185 </td> <td style="text-align:left;"> 0.626635989107761 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_male_df\)loop_pe_avg_m
0.939301920132143 0.0360882364913906 Shapiro-Wilk normality test midlife_HP_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.984861143748585 </td> <td style="text-align:left;"> 0.961650420612648 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_female_df\)loop_de_rad3_degree
0.673250270692041 0.000197903436428711 Shapiro-Wilk normality test midlife_HP_male_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.978359619965325 </td> <td style="text-align:left;"> 0.583947583573168 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_df\)loop_de_avg_degree
0.980483557611062 0.88401546577215 Shapiro-Wilk normality test midlife_HP_female_df\(loop_de_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.926780583374995 </td> <td style="text-align:left;"> 0.192192441466166 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_male_df\)loop_de_avg_degree
0.969348782601871 0.358539147520987 Shapiro-Wilk normality test midlife_HP_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.961043339052538 </td> <td style="text-align:left;"> 0.435645190326644 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_female_df\)loop_ae_rad3_degree
0.966162297290397 0.821589067567862 Shapiro-Wilk normality test midlife_HP_male_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.892510350444314 </td> <td style="text-align:left;"> 0.000747247688039471 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_df\)loop_ae_avg_degree
0.873682962552045 0.00424606303747878 Shapiro-Wilk normality test midlife_HP_female_df\(loop_ae_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.922588887317344 </td> <td style="text-align:left;"> 0.163331176169011 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_male_df\)loop_ae_avg_degree



Behavior summarized

loop_summarystats <- midlife_HP_df  %>% 
  group_by(sex) %>% 
  summarize(n_subject = n(),
            age_mean = mean(age_spatial_years),
            age_Sd = sd(age_spatial_years),
            AE_rad3 = mean(loop_ae_rad3_degree,na.rm=TRUE),
            AE_avg = mean(loop_ae_avg_degree ,na.rm=TRUE),
            PE_rad3 = mean(loop_pe_rad3_m,na.rm=TRUE),
            PE_avg = mean(loop_pe_avg_m,na.rm=TRUE),
            DT_rad3 = mean(loop_de_rad3_degree,na.rm=TRUE),
            DT_avg = mean(loop_de_avg_degree,na.rm=TRUE)) %>% as.data.frame() 
knitr::kable(loop_summarystats) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "200")
sex n_subject age_mean age_Sd AE_rad3 AE_avg PE_rad3 PE_avg DT_rad3 DT_avg
Female 26 50.23077 3.701974 69.8513 58.99396 3.130604 1.846265 393.4124 375.6190
Male 17 50.35294 3.920159 64.9243 51.29080 2.871218 1.632895 328.1449 338.1785

T1 adjust correlations

Shuying originally did an adjustment using TIV from VBM.

I don’t know if shuying and viasakh got the VBM for TIV the same. It looks like shuyings is multiplied by 1000x.

Due to that, I am correcting using Freesurfer TIV

#v contains adjusted hip
# 1 Create function for apply to variables
dividebyTIV <- function(x, na.rm = FALSE) (x/midlife_HP_df$eTIV)

# 2 Let's correct by mutating the columns using the TIV from freesurfer

midlife_HP_df_adj <- midlife_HP_df %>% mutate_at(vars(avg_t1_hipp, `Left-Hippocampus`, `Right-Hippocampus`),
            dividebyTIV) %>% 
  
  # multiplying to get proportions 
   mutate(avg_t1_hipp = avg_t1_hipp*1000,
         `Left-Hippocampus` = `Left-Hippocampus`*1000,
         `Right-Hippocampus` = `Right-Hippocampus`*1000)


midlife_HP_female_df_adj <- midlife_HP_df_adj %>% filter(sex=="Female")

midlife_HP_male_df_adj <- midlife_HP_df_adj %>%  filter(sex== "Male")
  • I need to create a correlation matrix to house all of the results from the correlations so I can correct p values later
midlife_HP_correlations <- data.frame(matrix(ncol=9, nrow=0)) 

Now that things have been adjusted I need to do correlations

total T1 HP

position Error

  • Position error avg is not significantly associated with t1 total hippocampus R=0.22, p=0.15
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t1_hipp,midlife_HP_df_adj$loop_pe_avg_m)) %>% mutate(analysis = "Avg_PE_T1total")

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1    0.223      1.47   0.150        41  -0.0828     0.491 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'


  • Position error rad3 is not significantly associated with t1 total hippocampus R=0.11, p=0.52
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t1_hipp,midlife_HP_df_adj$loop_pe_rad3_m)) %>% mutate(analysis = "rad3_PE_T1total")

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1    0.107     0.655   0.517        37   -0.216     0.409 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).


Angular Error

  • Angular error is not significantly associated with t1 total hippocampus R=0.066, p=0.67
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t1_hipp,midlife_HP_df_adj$loop_ae_avg_degree, method = "spearman")) %>% mutate(analysis = "Avg_AE_T1total") %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")
x <- x[,c(1,2,3,7,8,9,4,5,6)]

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1   0.0658     12372   0.674 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'



  • Angular error is not significantly associated with t1 total hippocampus R=0.1, p=0.54
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t1_hipp,midlife_HP_df_adj$loop_ae_rad3_degree)) %>% mutate(analysis = "rad3_AE_T1total")

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1    0.100     0.612   0.544        37   -0.222     0.403 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

Degrees Traveled

  • Degrees Traveled is not significantly associated with t1 total hippocampus R=-0.17, p=0.28
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t1_hipp,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T1total")

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.167     -1.08   0.285        41   -0.445     0.140 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'


  • degrees traveled is not significantly associated with t1 total hippocampus R=-0.079, p=0.63
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t1_hipp,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T1total") %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")
x <- x[,c(1,2,3,7,8,9,4,5,6)]

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1  -0.0789     10660   0.632 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

Left HP

** position Error **

  • Position error avg is not significantly associated with t1 left hippocampus R=0.22, p=0.16
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Position error rad3 is not significantly associated with t1 left hippocampus R=0.012, p=0.48
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).


** Angular Error**

  • Angular error is not significantly associated with t1 left hippocampus R=0.029, p=0.057
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Angular error is not significantly associated with t1 total hippocampus R=0.11, p=0.52
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not significantly associated with t1 Left-Hippocampus R=-0.015, p=0.32
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • degrees traveled is not significantly associated with t1 Left-Hippocampus R=-0.051, p=0.76
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

Right HP

** position Error **

  • Position error avg is not significantly associated with t1 Right hippocampus R=0.22, p=0.15
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Position error rad3 is not significantly associated with t1 right hippocampus R=0.0941, p=0.57
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).


** Angular Error**

  • Angular error is not significantly associated with t1 right hippocampus R=0.029, p=0.062
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Angular error is not significantly associated with t1 right hippocampus R=-0.093, p=0.58
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not significantly associated with t1 Right-Hippocampus R=-0.0182, p=0.26
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • degrees traveled is not significantly associated with t1 Right-Hippocampus R=-0.09, p=0.58
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

total T2 HP

position Error

  • Position error avg is not significantly associated with t2 total hippocampus R=0.048, p=0.82
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t2_total_hipp,midlife_HP_df_adj$loop_pe_avg_m)) %>% mutate(analysis = "avg_PE_T2total")

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   0.0476     0.234   0.817        24   -0.346     0.427 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t2_total_hipp", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).


  • Position error rad3 is not significantly associated with t2 total hippocampus R=-0.11, p=0.61
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t2_total_hipp,midlife_HP_df_adj$loop_pe_rad3_m)) %>% mutate(analysis = "rad3_PE_T2total")

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.113    -0.523   0.607        21   -0.502     0.313 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t2_total_hipp", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).



Angular Error

  • Angular error is not significantly associated with t2 total hippocampus R=0.14, p=0.49
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t2_total_hipp,midlife_HP_df_adj$loop_ae_avg_degree, method = "spearman")) %>% mutate(analysis = "Avg_AE_T2total") %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")
x <- x[,c(1,2,3,7,8,9,4,5,6)]

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1    0.141      2512   0.490 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t2_total_hipp", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).


  • Angular error is not significantly associated with t2 total hippocampus R=-0.11, p=0.62
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t2_total_hipp,midlife_HP_df_adj$loop_ae_rad3_degree)) %>% mutate(analysis = "rad3_AE_T2total" )

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.110    -0.505   0.618        21   -0.499     0.317 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t2_total_hipp", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).



Degrees Traveled

  • Degrees Traveled is significantly associated with t2 total hippocampus R=-0.053, p=0.0049
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t2_total_hipp,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2total" )

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.535     -3.10 0.00489        24   -0.764    -0.186 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t2_total_hipp", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).


  • degrees traveled is not significantly associated with t2 total hippocampus R=-0.67, p=0.00061
x <- broom::tidy(cor.test(midlife_HP_df_adj$avg_t2_total_hipp,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2total" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]

midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic  p.value parameter conf.low conf.high method    alternative
##      <dbl>     <dbl>    <dbl> <chr>     <chr>    <chr>     <chr>     <chr>      
## 1   -0.672      3384 0.000615 NA        NA       NA        Spearman… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t2_total_hipp", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

CA1

Degrees Traveled

  • Degrees Traveled is significantly associated with ca1 R=-0.53, p=0.0058
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_ca1,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2CA1" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.526     -3.03 0.00578        24   -0.759    -0.174 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_ca1", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).

  • Degrees Traveled rad3 is significantly associated with ca1 R=-0.7, p=0.00032
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_ca1,midlife_HP_df_adj$loop_de_rad3_degree)) %>% mutate(analysis = "rad3_DT_T2CA1" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.596     -3.40 0.00267        21   -0.810    -0.244 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_ca1", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

CA23
  • Degrees Traveled is significantly associated with ca23 R=-0.54, p=0.0047
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_ca23,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2CA23" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.537     -3.12 0.00466        24   -0.765    -0.189 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_ca23", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).

x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_ca23,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2CA23" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1   -0.635      3310 0.00144 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
  • Degrees Traveled rad3 is significantly associated with ca23 hippocampus R=-0.64, p=0.0014
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_ca23", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

DG

Degrees Traveled is significantly associated with DG R=-0.46, p=0.017

x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_dg,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2DG" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.465     -2.57  0.0167        24   -0.722   -0.0946 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_dg", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).


  • Degrees Traveled rad3 is significantly associated with t2 total hippocampus R=-0.64, p=0.0014
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_dg,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2DG" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1   -0.541      3118 0.00865 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_dg", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

sub
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_sub,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2sub" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   -0.410     -2.20  0.0377        24   -0.688   -0.0265 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>

Degrees Traveled is significantly associated with DG R=-0.41, p=0.038

# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_sub", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).

  • Degrees Traveled rad3 is significantly associated with t2 total hippocampus R=-0.69, p=0.00043
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_sub,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2SUB" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic  p.value parameter conf.low conf.high method    alternative
##      <dbl>     <dbl>    <dbl> <chr>     <chr>    <chr>     <chr>     <chr>      
## 1   -0.686      3412 0.000431 NA        NA       NA        Spearman… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_sub", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

EC
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_erc,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2ERC" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   0.0513     0.252   0.803        24   -0.343     0.430 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>

Degrees Traveled is significantly associated with DG R=-0.051, p=0.08

# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_erc", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).

x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_erc,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2ERC" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1   0.0919      1838   0.676 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
  • Degrees Traveled rad3 is significantly associated with t2 total hippocampus R=0.092, p=0.68
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_erc", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

PHC
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_phc,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2PHC" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)
x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   0.0643     0.316   0.755        24   -0.331     0.441 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>

Degrees Traveled is significantly associated with DG R=0.064, p=0.75

# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_phc", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).

x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_phc,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2PHC" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)
x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1   -0.112      2250   0.611 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
  • Degrees Traveled rad3 is significantly associated with t2 total hippocampus R=-0.11, p=0.61
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_phc", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

PRC
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_prc,midlife_HP_df_adj$loop_de_avg_degree)) %>% mutate(analysis = "Avg_DT_T2PRC" )  


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl>     <int>    <dbl>     <dbl> <chr>      <chr>      
## 1   0.0167    0.0816   0.936        24   -0.373     0.401 Pearson's… two.sided  
## # … with 1 more variable: analysis <chr>

Degrees Traveled is significantly associated with DG R=0.064, p=0.75

# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_prc", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 17 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 17 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 17 rows containing missing values (`geom_point()`).

  • Degrees Traveled rad3 is significantly associated with t2 total hippocampus R=0.1, p=0.64
x <- broom::tidy(cor.test(midlife_HP_df_adj$t2hipp_vol_avg_prc,midlife_HP_df_adj$loop_de_rad3_degree, method = "spearman")) %>% mutate(analysis = "rad3_DT_T2PRC" )  %>% mutate(parameter = "NA", conf.low= "NA", conf.high= "NA")

x <- x[,c(1,2,3,7,8,9,4,5,6)]


midlife_HP_correlations <- rbind(midlife_HP_correlations,x)

x
## # A tibble: 1 × 9
##   estimate statistic p.value parameter conf.low conf.high method     alternative
##      <dbl>     <dbl>   <dbl> <chr>     <chr>    <chr>     <chr>      <chr>      
## 1    0.103      1816   0.640 NA        NA       NA        Spearman'… two.sided  
## # … with 1 more variable: analysis <chr>
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "t2hipp_vol_avg_prc", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 20 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).

Summary

knitr::kable(midlife_HP_correlations) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
estimate statistic p.value parameter conf.low conf.high method alternative analysis
0.2231003 1.4654759 0.1504186 41 -0.0827910798748028 0.490572505346509 Pearson’s product-moment correlation two.sided Avg_PE_T1total
0.1070264 0.6547769 0.5166615 37 -0.215777185143937 0.408740797642151 Pearson’s product-moment correlation two.sided rad3_PE_T1total
0.0658411 12372.0000000 0.6739377 NA NA NA Spearman’s rank correlation rho two.sided Avg_AE_T1total
0.1001617 0.6123394 0.5440584 37 -0.222383236790673 0.402944631762104 Pearson’s product-moment correlation two.sided rad3_AE_T1total
-0.1669103 -1.0839529 0.2847190 41 -0.44494865482203 0.140475691477639 Pearson’s product-moment correlation two.sided Avg_DT_T1total
-0.0789474 10660.0000000 0.6317372 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T1total
0.0476246 0.2335769 0.8172943 24 -0.346112248374205 0.427097653079842 Pearson’s product-moment correlation two.sided avg_PE_T2total
-0.1133522 -0.5228148 0.6065758 21 -0.502094531545537 0.313497820728366 Pearson’s product-moment correlation two.sided rad3_PE_T2total
0.1411966 2512.0000000 0.4897700 NA NA NA Spearman’s rank correlation rho two.sided Avg_AE_T2total
-0.1096367 -0.5054655 0.6184962 21 -0.49927537657276 0.316886363386676 Pearson’s product-moment correlation two.sided rad3_AE_T2total
-0.5346606 -3.0995077 0.0048933 24 -0.763823338911977 -0.185785098849752 Pearson’s product-moment correlation two.sided Avg_DT_T2total
-0.6719368 3384.0000000 0.0006146 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2total
-0.5260197 -3.0300337 0.0057777 24 -0.758768899396511 -0.174152127363703 Pearson’s product-moment correlation two.sided Avg_DT_T2CA1
-0.5962748 -3.4037629 0.0026743 21 -0.80951073283641 -0.244058751519735 Pearson’s product-moment correlation two.sided rad3_DT_T2CA1
-0.5371803 -3.1200197 0.0046581 24 -0.765291926604219 -0.189195761533215 Pearson’s product-moment correlation two.sided Avg_DT_T2CA23
-0.6353755 3310.0000000 0.0014410 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2CA23
-0.4648803 -2.5722919 0.0167174 24 -0.722186481155828 -0.0945551391241389 Pearson’s product-moment correlation two.sided Avg_DT_T2DG
-0.5405138 3118.0000000 0.0086502 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2DG
-0.4096148 -2.1996995 0.0377007 24 -0.687831867271838 -0.0264614055838992 Pearson’s product-moment correlation two.sided Avg_DT_T2sub
-0.6857708 3412.0000000 0.0004310 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2SUB
0.0513173 0.2517341 0.8033897 24 -0.342849747515908 0.430119396449599 Pearson’s product-moment correlation two.sided Avg_DT_T2ERC
0.0918972 1838.0000000 0.6758009 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2ERC
0.0643251 0.3157812 0.7548984 24 -0.331281221294004 0.440696419402428 Pearson’s product-moment correlation two.sided Avg_DT_T2PHC
-0.1116601 2250.0000000 0.6107559 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2PHC
0.0166508 0.0815832 0.9356547 24 -0.373107579845289 0.401413731242399 Pearson’s product-moment correlation two.sided Avg_DT_T2PRC
0.1027668 1816.0000000 0.6397044 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2PRC
Avg_DT_correlations <- midlife_HP_correlations %>% filter(grepl("DT",analysis)) %>% filter(grepl("Avg",analysis)) %>% filter(!grepl("total",analysis)) %>% mutate(correctedPvalue = p.adjust(p.value, method = "BH", n = 7))


rad3_DT_correlations <- midlife_HP_correlations %>% filter(grepl("DT",analysis)) %>% filter(grepl("rad3",analysis)) %>% filter(!grepl("total",analysis)) %>% mutate(correctedPvalue = p.adjust(p.value, method = "BH", n = 7))
knitr::kable(Avg_DT_correlations) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
estimate statistic p.value parameter conf.low conf.high method alternative analysis correctedPvalue
-0.5260197 -3.0300337 0.0057777 24 -0.758768899396511 -0.174152127363703 Pearson’s product-moment correlation two.sided Avg_DT_T2CA1 0.0202219
-0.5371803 -3.1200197 0.0046581 24 -0.765291926604219 -0.189195761533215 Pearson’s product-moment correlation two.sided Avg_DT_T2CA23 0.0202219
-0.4648803 -2.5722919 0.0167174 24 -0.722186481155828 -0.0945551391241389 Pearson’s product-moment correlation two.sided Avg_DT_T2DG 0.0390072
-0.4096148 -2.1996995 0.0377007 24 -0.687831867271838 -0.0264614055838992 Pearson’s product-moment correlation two.sided Avg_DT_T2sub 0.0659762
0.0513173 0.2517341 0.8033897 24 -0.342849747515908 0.430119396449599 Pearson’s product-moment correlation two.sided Avg_DT_T2ERC 0.9356547
0.0643251 0.3157812 0.7548984 24 -0.331281221294004 0.440696419402428 Pearson’s product-moment correlation two.sided Avg_DT_T2PHC 0.9356547
0.0166508 0.0815832 0.9356547 24 -0.373107579845289 0.401413731242399 Pearson’s product-moment correlation two.sided Avg_DT_T2PRC 0.9356547
knitr::kable(rad3_DT_correlations) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
estimate statistic p.value parameter conf.low conf.high method alternative analysis correctedPvalue
-0.5962748 -3.403763 0.0026743 21 -0.80951073283641 -0.244058751519735 Pearson’s product-moment correlation two.sided rad3_DT_T2CA1 0.0062401
-0.6353755 3310.000000 0.0014410 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2CA23 0.0050436
-0.5405138 3118.000000 0.0086502 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2DG 0.0151379
-0.6857708 3412.000000 0.0004310 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2SUB 0.0030173
0.0918972 1838.000000 0.6758009 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2ERC 0.6758009
-0.1116601 2250.000000 0.6107559 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2PHC 0.6758009
0.1027668 1816.000000 0.6397044 NA NA NA Spearman’s rank correlation rho two.sided rad3_DT_T2PRC 0.6758009


Hormone Correlations

Estradiol

Here I am looking at hormones but only for the LOOP group and not all women with a T1 Scan. SO instead of n =74 like shuying, its n = 26

T1 total hippocampus

Estradiol was not significantly associated with total T1 hippocampal volume ( rs(24) = 0.065, p = 0.75)

broom::tidy(cor.test(midlife_HP_female_df_adj$avg_t1_hipp,midlife_HP_female_df_adj$estradiol_spatial_pg_ml, method = "spearman")) 
## # A tibble: 1 × 5
##   estimate statistic p.value method                          alternative
##      <dbl>     <dbl>   <dbl> <chr>                           <chr>      
## 1   0.0653      2734   0.751 Spearman's rank correlation rho two.sided
ggscatter(midlife_HP_female_df_adj, x = "avg_t1_hipp", y = "estradiol_spatial_pg_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Estradiol", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'


T2 total hippocampus

estradiol was not significantly associated with T2 total hippocampal volume r = -0.29, p=0.29

ggscatter(midlife_HP_female_df_adj, x = "avg_t2_total_hipp", y = "estradiol_spatial_pg_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Estradiol", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 11 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 11 rows containing missing values (`geom_point()`).


FSH

FSH was not significantly associated with total T1 hippocampal volume ( rs(24) = -0.16, p = 0.44)

ggscatter(midlife_HP_female_df_adj, x = "avg_t1_hipp", y = "fsh_spatial_miu_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "FSH", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

T2 total hippocampus

FSH was not significantly associated with T2 total hippocampal volume

ggscatter(midlife_HP_female_df_adj, x = "avg_t2_total_hipp", y = "fsh_spatial_miu_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "FSH", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 11 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 11 rows containing missing values (`geom_point()`).

Progesterone

T1 total hippocampus Progesterone was not significantly associated with total T1 hippocampal volume ( rs(24) = 0.2, p = 0.32)

ggscatter(midlife_HP_female_df_adj, x = "avg_t1_hipp", y = "progesterone_spatial_ng_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "progesterone" ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'


T2 total hippocampus

progesterone was not significantly associated with T2 total hippocampal volume r = -0.21 p=045

ggscatter(midlife_HP_female_df_adj, x = "avg_t2_total_hipp", y = "progesterone_spatial_ng_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "prog") +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 11 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 11 rows containing missing values (`geom_point()`).


avg ca1

progesterone was not significantly associated with T2 total hippocampal volume r = -0.21 p=045

ggscatter(midlife_HP_female_df_adj, x = "t2hipp_vol_avg_ca1", y = "progesterone_spatial_ng_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "prog") +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 11 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 11 rows containing missing values (`geom_point()`).

avg ca23

avg DG

ggscatter(midlife_HP_female_df_adj, x = "t2hipp_vol_avg_dg", y = "progesterone_spatial_ng_ml",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "prog") +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 11 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 11 rows containing missing values (`geom_point()`).

Young

For young adults hippocampal., we will use freesurfer and VBM. We need to put things into scale with the midlife

Creating HP specific Dataframe : young

Total N - 31

# Let's create a clean df to work with here and include only the columns we want 

young_HP_df <-
 young_raw_df %>%  dplyr::select(
    "subject_id",
    "sex",
    "age_spatial_years",
    "loop_pe_rad3_m",
    "loop_pe_avg_m",
    "loop_de_rad3_degree",
    "loop_de_avg_degree",
    "loop_ae_rad3_degree",
    "loop_ae_avg_degree",
    "Left-Hippocampus",
    "Right-Hippocampus",
    "eTIV",
    "VaisTIV_VBM"
  ) %>% mutate(avg_t1_hipp = (.$`Left-Hippocampus` + .$`Right-Hippocampus`) /
                 2) %>% filter(!is.na(eTIV)) # Need to make sure we remove subj without scan # N=43
  
young_HP_female_df <- young_HP_df %>% filter(sex=="Female")

young_HP_male_df <- young_HP_df %>%  filter(sex== "Male")
 

Checking normality

knitr::kable(normality_young_HP) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
statistic pvalue method variable
0.891289448362091 0.0406097060033547 Shapiro-Wilk normality test young_HP_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.917622398774867 </td> <td style="text-align:left;"> 0.410869741382047 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_female_df\)loop_pe_rad3_m
0.819554493642225 0.0250296107499683 Shapiro-Wilk normality test young_HP_male_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.924692504181416 </td> <td style="text-align:left;"> 0.031503217012436 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_df\)loop_pe_avg_m
0.942325549335712 0.548211885252153 Shapiro-Wilk normality test young_HP_female_df\(loop_pe_avg_m </td> </tr> <tr> <td style="text-align:left;"> 0.847440833424306 </td> <td style="text-align:left;"> 0.00483057995234475 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_male_df\)loop_pe_avg_m
0.944415104032301 0.344201630583619 Shapiro-Wilk normality test young_HP_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.938516537642211 </td> <td style="text-align:left;"> 0.596545695693001 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_female_df\)loop_de_rad3_degree
0.8802028757242 0.131195427127208 Shapiro-Wilk normality test young_HP_male_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.955146425146202 </td> <td style="text-align:left;"> 0.216072493905156 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_df\)loop_de_avg_degree
0.919874033596885 0.317592303908437 Shapiro-Wilk normality test young_HP_female_df\(loop_de_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.924156119557848 </td> <td style="text-align:left;"> 0.119146358186268 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_male_df\)loop_de_avg_degree
0.829226875499169 0.00405239215676323 Shapiro-Wilk normality test young_HP_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.869160922556705 </td> <td style="text-align:left;"> 0.147906350957043 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_female_df\)loop_ae_rad3_degree
0.743377568430897 0.0029639851230227 Shapiro-Wilk normality test young_HP_male_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.890867713779674 </td> <td style="text-align:left;"> 0.00429709005526902 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_df\)loop_ae_avg_degree
0.918178775417547 0.303762177188411 Shapiro-Wilk normality test young_HP_female_df\(loop_ae_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.825228129557514 </td> <td style="text-align:left;"> 0.00210709055367868 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_male_df\)loop_ae_avg_degree



young_loop_summarystats <- young_HP_df  %>% 
  group_by(sex) %>% 
  summarize(n_subject = n(),
            age_mean = mean(age_spatial_years),
            age_Sd = sd(age_spatial_years),
            AE_rad3 = mean(loop_ae_rad3_degree,na.rm=TRUE),
            AE_avg = mean(loop_ae_avg_degree ,na.rm=TRUE),
            PE_rad3 = mean(loop_pe_rad3_m,na.rm=TRUE),
            PE_avg = mean(loop_pe_avg_m,na.rm=TRUE),
            DT_rad3 = mean(loop_de_rad3_degree,na.rm=TRUE),
            DT_avg = mean(loop_de_avg_degree,na.rm=TRUE)) %>% as.data.frame() 
knitr::kable(young_loop_summarystats) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "200")
sex n_subject age_mean age_Sd AE_rad3 AE_avg PE_rad3 PE_avg DT_rad3 DT_avg
Female 11 20.81818 2.561959 63.95039 52.86314 2.851491 1.589096 362.7044 368.8774
Male 20 20.25000 2.531382 54.86657 44.32274 2.487910 1.273323 352.4536 364.8809

Adjust TIV correlations

Shuying originally did an adjustment. so that’s waht we’re doing below. First we need to correct the T1 hippocampal volumes for TIV. T1 volumes and TIV are coming from freesurfers recon all.

# Okay so we need to do a bit of changing here by bringing our VBM to scale with midlife 
young_HP_df_adj <- young_HP_df %>%  mutate(VaisTIV_VBM = VaisTIV_VBM*1000)


#v contains adjusted hip
# now we create the function for adjusting by TIV
# 1 Create function for apply to variables
Young_dividebyTIV <- function(x, na.rm = FALSE) (x/young_HP_df_adj$VaisTIV_VBM)

# 2 Let's correct by mutating the columns using the TIV from freesurfer

young_HP_df_adj <- young_HP_df_adj %>% mutate_at(vars(avg_t1_hipp, `Left-Hippocampus`, `Right-Hippocampus`),
            Young_dividebyTIV) %>% 
  
  # multiplying to get proportions 
   mutate(avg_t1_hipp = avg_t1_hipp*1000,
         `Left-Hippocampus` = `Left-Hippocampus`*1000,
         `Right-Hippocampus` = `Right-Hippocampus`*1000)
Total HP

position Error

  • position error is not associated with T1 hippocampal volume in young adults R=-0.061, p=0.75 -not normal using spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Position error rad3 is not associated with position error in young adults R=-0.052, p=0.84
  • not normal, using spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).


** Angular Error **

  • Angular error is not associated with T1 hippocampal volume in young adults R=-0.068, p=0.72 -not normal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with position error in young adults. R=-0.52, p=0.84
  • not normal using spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not associated with T1 hippocampal volume in young adults R= - 0.2, p=0.28
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with position error in young adults. R=-0.17, p=0.52
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).

Left

** position Error **

  • position error is not associated with T1 left hippocampal volume in young adults R=-0.072, p=0.71
  • PE not normal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Position error rad3 is not associated with left hippocampal volume in young adults R=-0.012, p=0.65
  • not normal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).


** Angular Error **

  • Angular error is not associated with T1 left hippocampal volume in young adults R=-0.099, p=0.6
  • notnormal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with left hippocampal volume in young adults. R=0.012, p=0.65

-not normal use spearman

# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not associated with T1 left hippocampal volume in young adults R= -0.18, p=0.34
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with left hippocampal volume in young adults. R=-0.092, p=0.72
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).